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A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.
| Version: | 1.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | ggplot2, ClusterR, Rfast, diptest |
| Suggests: | knitr |
| Published: | 2020-02-10 |
| Author: | Christopher R John, David Watson |
| Maintainer: | Christopher R John <chris.r.john86 at gmail.com> |
| License: | AGPL-3 |
| NeedsCompilation: | no |
| In views: | Cluster |
| CRAN checks: | Spectrum results |
| Reference manual: | Spectrum.pdf |
| Vignettes: |
Spectrum |
| Package source: | Spectrum_1.1.tar.gz |
| Windows binaries: | r-devel: Spectrum_1.1.zip, r-release: Spectrum_1.1.zip, r-oldrel: Spectrum_1.1.zip |
| macOS binaries: | r-release (arm64): Spectrum_1.1.tgz, r-oldrel (arm64): Spectrum_1.1.tgz, r-release (x86_64): Spectrum_1.1.tgz, r-oldrel (x86_64): Spectrum_1.1.tgz |
| Old sources: | Spectrum archive |
| Reverse imports: | MMOC |
| Reverse suggests: | aPEAR, FCPS |
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